Currently, the generation of dynamic content relies on either simplistic static replacement of fields with text, like the mail merge function in popular word processing programs, or on resource-intensive natural language processing using complex artificial intelligence.
Field-based static text replacement is limited in terms of the depth of its output. Lacking any ability to parse logical rules, this product is limited to simple replacement of “fields” (representing locations where data should be inserted) in a text template with text or numeric data values stored in a database. For example, this field-based static replacement product could replace the template phrase “[First Name] likes to run with [Best Friend]” with the auto generated phrases “Alice likes to run with Bob” and “Charlie likes to run with Dave.” Static text replacement could not, however, alter this template based on variations in the data to which it is applied. This forces a one-size-fits-all solution to dynamic text generation. In the above example, assume the user instead created the template phrase “[First Name] likes to run with [Friend 1] and [Friend 2].” If static text replacement applied this template to a data record stating “First Name=Alice, Friend 1=Bob, Friend 2=Earl,” the product would create the phrase “Alice likes to run with Bob and Earl.” But if the same template applied to a data record stating “First Name=Charlie, Friend 1=Dave,” where the Friend 2 data field was empty (perhaps because Charlie only liked to run with one friend), the product would then generate the grammatically-incorrect “Charlie likes to run with Dave and.”
Natural language processing using artificial intelligence avoids the limitations of static text and allows dynamic generation of more complex text and other communications. Using natural language processing, a system can dynamically generate text based on grammatical or statistical models of language in a particular domain. These models, however, require a high-level of operational skill to create, and are domain-specific (i.e., a product that allows the dynamic generation of form letters to finance customers is unhelpful when generating medical reports). End-users who find that a natural language product does not adequately support their given domain of expertise must seek out computer science professionals to build the appropriate models for their field, which can become costly and impractical.
There is still a need to dynamically generate text content, and to customize the content of that text based on the data being used to generate it, in order to provide a system that allows content authors to personalize content designed for distribution to a variety of end users. What is needed is a system and method providing a scalable technology to allow a broader group of users to be able to script the automated generation of dynamic text.